Unless otherwise indicated herein, the approaches described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.
Extract, transform, and load (ETL) processing is an ongoing effort in any large enterprise information system, in which data stored in database tables are moved from one location (one or more source database tables) to another location (target database table). A well-executed ETL job begins with a good understanding of the data being moved about and a good dataflow design for extracting the data and transforming the data so that it winds up in the target database table in a meaningful and useful way. Typical ETL design tools use metadata-driven views of the data that are being moved. Accordingly, ETL designers are familiar with viewing the data to be moved in terms of the metadata that describe the data. Users of the data, however, may not feel as comfortable viewing their data in terms of the underlying metadata. Viewing the actual data that is stored in the database table versus the metadata that characterize the actual data is more intuitive for the user, and for ETL designer may enhance the traditional metadata view that they are accustomed to.